A significant challenge facing all businesses is managing the constantly changing business environment. With the advent of the World Wide Web, the rate of change has accelerated dramatically when compared to what previously was considered a grueling pace. Changes to the businesses place enormous stress not only on employees, but also on the business' enterprise applications.
All organizations operate from a business model. This model is a defined set of organizational structures and interrelating business rules. It is also the go-to-market strategy for the business. Operational systems are driven from an understanding of this business model and when the rapid pace of change is considered along with the complexity of these systems, it becomes apparent that it is very difficult to keep operational systems current with the business' go-to-market strategy.
A significant outcome of Data Warehousing initiatives has been the consolidation of various islands of information in corporate IT environments. Transaction systems continue to produce large volumes of data and the proliferation of web Intranets has hastened the pace of data production. A deeper penetration of information systems within organizations has resulted in data encountering many processing cycles before it settles into a data warehouse. Similarly, an ever increasing numbers of user applications—report writers, OLAP and web tools—are mining the data in the data warehouse. In other words, although data warehouses are consolidating business intelligence information, the corporate data is flowing through a web of interfaces, i.e. information supply chain, that are longer, deeper and more complex than ever before.
Since data at every point of its life cycle is an outcome of a set of business rules and operations, a slight change in the business model impacts the meaning and validity of data at every point in the organization. This has a direct bearing on the rules coded in a data warehouse for extracting business intelligence information from raw data. The business model is typically only taken into account at the beginning of the project, the start of the information supply chain. Herein lies the problem of temporal distortion. Temporal distortion results from the inability of enterprise applications to keep pace with the ever-changing business environment. Each enterprise application, including data warehouses as cited in this example, maintain an independent view of the business model that is quickly invalidated by the rapid pace of business change. Furthermore, because enterprise applications are usually installed serially, they each maintain a different view of the business model. They are operating with different snapshots of the then-current business model.
Using the data warehouse as an example, the current view of information could be temporally distorted as compared to the historical views because of a changed business model. Additionally, organizations are often required to restate historical information (e.g. financial information) to comply with changing reporting requirements and/or discovery of new facts. Restating history is also a temporal distortion of data.
The net result is that the keepers of Data Warehouses are making compromised comparisons between historical views and the current view of data. Even if the information supply chain is to be brought into compliance with the revised business model, the process is cumbersome, time-consuming, and expensive with current technology and tools. The unfortunate result is that by the time such changes are instituted, the underlying business model could have changed again, thus rendering the just-completed effort obsolete. The only alternative, generally considered unacceptable to the business, is to stop changing the business. As alluded to above, the costs of conducting such changes are exorbitant. These costs can be a substantial percentage of the original cost of building and deploying the data warehouse, recurring annually.
Thus, there exists a need for improving data structures and related data handling systems for support of business operations.